The need to detect and track targets from moving platforms is driving the development of sensor fusion and computer vision algorithms for next-generation situational awareness and navigation systems. For instance, safely landing an aircraft requires accurate information about the location of the runway while military targeting systems for urban environments require accurate localization to avoid collateral damage and civilian casualties.
Recent research efforts have attempted to analyze sensor data using moving platforms to enable the commander, with additional features, to easily navigate or identify potential hazards or imminent threats, to avoid these hazards, and to obtain sufficient visual reference of the actual target. In addition, researchers analyzing moving platform cameras have had to deal with further shortcomings, such as the competing background clutter, changing background dynamics and artifacts due to motion.
On the other hand, the detection of targets is generally carried out through the implementation of segmentation and tracking techniques based on image correlation as maintained by a static platform hypothesis or static scene hypothesis and subsequently, by analyzing the resultant sequence of frames independently. When there are significant dynamics in the scene or the target is moving rapidly, this type of analysis usually results in jitter artifacts due to incomplete motion compensation from the sensor and/or the platform.
Thus, there is a need to analyze real-time sensor images from moving platforms while taking into account the platform and sensor dynamics to compensate for the motion to allow an accurate estimate of the target location. This provides the operator with the visual references of the target needed to act appropriately, whether for safe navigation, targeting enemies, or avoiding hazards. This apparatus provides accurate visual cues from sensor images based on a combined solution that takes into account the system dynamics and the observation measurements.